Solving optimization problems using heuristic algorithms requires the selection of its parameters. Traditionally, these parameters are selected by a trial and error process that cannot guarantee the quality of the results obtained because not all the potential combinations of parameters are checked. To fill this gap, this paper proposes the application of Taguchi's orthogonal arrays to calibrate the parameters of a heuristic optimization algorithm (the descent local search algorithm). This process is based on the study of the combinations of discrete values of the heuristic tool parameters and it enables optimization of the heuristic tool performance with a reduced computational effort. To check its efficiency, this methodology is applied to a technical challenge never studied before: the optimization of the tensioning process of cable‐stayed bridges. The statistical improvement of the heuristic tool performance is studied by the optimization of the tensioning process of a real cable‐stayed bridge. Results show that the proposed calibration technique provided robust values of the objective function (with lower minimum and mean values, and lower standard deviation) with reduced computational cost.

中文翻译：

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使用正交阵列校准后裔局部搜索算法参数

使用启发式算法解决优化问题需要选择其参数。传统上，这些参数是通过试错法选择的，由于不能检查所有可能的参数组合，因此不能保证所获得结果的质量。为了填补这一空白，本文提出了Taguchi正交数组在校准启发式优化算法（下降局部搜索算法）的参数中的应用。该过程基于对启发式工具参数的离散值的组合的研究，并且能够以减少的计算量来优化启发式工具的性能。为了检查其效率，此方法应用于从未研究过的技术挑战：优化斜拉桥的张紧过程。通过优化实际斜拉桥的张紧过程，研究了启发式工具性能的统计改进。结果表明，所提出的校准技术提供了目标函数的鲁棒值（具有较低的最小值和平均值，以及较低的标准偏差），同时降低了计算成本。